Nanoscale silicate melt textures determine volcanic ash surface chemistry

Explosive volcanic eruptions produce vast quantities of silicate ash, whose surfaces are subsequently altered during atmospheric transit. These altered surfaces mediate environmental interactions, including atmospheric ice nucleation, and toxic effects in biota. A lack of knowledge of the initial, pre-altered ash surface has required previous studies to assume that the ash surface composition created during magmatic fragmentation is equivalent to the bulk particle assemblage. Here we examine ash particles generated by controlled fragmentation of andesite and find that fragmentation generates ash particles with substantial differences in surface chemistry. We attribute this disparity to observations of nanoscale melt heterogeneities, in which Fe-rich nanophases in the magmatic melt deflect and blunt fractures, thereby focusing fracture propagation within aureoles of single-phase melt formed during diffusion-limited growth of crystals. In this manner, we argue that commonly observed pre-eruptive microtextures caused by disequilibrium crystallisation and/or melt unmixing can modify fracture propagation and generate primary discrepancies in ash surface chemistry, an essential consideration for understanding the cascading consequences of reactive ash surfaces in various environments.

1) Description and preliminary interpretation of ash surface surface composition with fragmentation mode and experimental pressure and temperature 2) Discussion on the effect of boundary layer chemistry on viscosity and fracture toughness.
3) Discussion on the effect of nanophase formation in the matrix melt on viscosity and eruption triggering.4) Natural conditions for development of Fe-Ti nanotextures 5) Comparison of results derived from different measurement techniques.

6) XPS measurement experiments and data analysis integrity
Supplementary tables 1-4 1) X-ray photoelectron spectroscopy (XPS) elemental data from the 2-10 nm surface of experimental ash particles.2) Bulk and micro-XRF measurements of crushed and fragmented experimental materials.
3) QEMSCAN phase quantification results.4) Mean elemental composition of identified mineral phases.5) Comparison of EPMA measurements for matrix glass and boundary-layer glass and calculated 'ideal' bulk glass.6) Calculated near-surface and bulk compositions based on combined minerology and EPMA data.

Supplementary figures 1-8
1) Variable magnification SEM-BSE images of experimental pyroclasts showing matrix textures and mineralogy in the experimental pyroclasts.2) Ratio between average XPS surface chemistry and average bulk chemistry 3) SEM-EDX spot and linescan measurements through the compositional boundary layers surrounding pyroxene microlites 4) Comparison of textures between pyroclasts formed at room temperature and 850 o C.G 5) A comparison of clast types and textures in natural (a-c) and experimental (d-f) pyroclasts.6) Textures of natural pyroclastic fallout samples.7) Qualitative SEM-BSE observations of fracture pathways in experimental pyroclasts.8) Comparison of average elemental concentration from methods used in the study.

Description and preliminary interpretation of ash surface surface composition with fragmentation mode and experimental pressure and temperature
Variations in particle surface chemistry with fragmentation mode, P and T are generated from the interaction of the physical (i.e.size, shape and distribution) and mechanical properties of constituent phases with the stress field and fracture energy released during the fragmentation process 1 .
Direct comparison of the bulk and nanoscale surface measurements in Supplementary Figure 8 suggest that the depth dependence for concentration (i.e., the difference between XPS measurements and microscale or bulk measurements) and the sensitivity to fragmentation mode varies for different elements.The most depth-dependent elemental concentrations appear to be Mg, K, Na and Fe, in that order.These elements also show variations in concentration in the EDS-linescans between compositional boundary layers and microlite-bearing glass in Supplementary Figure 3.A dependence on fragmentation mode is found for all elements, but is most pronounced and consistent for Mg, Na and K where, in all cases, crushed samples have higher concentration at the nm-scale than samples fragmented by rapid decompression under the same conditions.This pattern is also found at the micron scale for K, but not Mg or Na (see Figure 2a, c and Supplementary Figure 8).
Plagioclase is a phase that is depleted at particle boundaries for all experiments, in line with similar observations in natural samples 2 , but we observe higher fractions of (Na-rich) plagioclase (see Supplementary Table 3 for phase compositions) and a corresponding increase in Na at particle surfaces formed by crushing.Changes in the experimental conditions show no consistent effect with pressure, while higher temperature is associated with slight enrichment of Fe.
We note that lower fracture energies during fragmentation result in an increased sensitivity of fracture paths to mechanical properties and material texture 3 , and we infer that higher fracture energy is imparted during shock tube experiments, but further focused experimental and analytical work is required to interpret and classify variations in surface composition generated from varying fragmentation processes and P-T conditions.

Effect of boundary layer chemistry on viscosity and fracture toughness.
As a first-order approximation, the boundary layer contains fewer network-modifying cations (Mg and Fe) and more Al 3+ charge-compensating alkalis (Na and K) 4 than the matrix glass, therefore it is expected to increase polymerisation and lower the fracture toughness.Accurate chemical analysis is hampered by activation volumes (SEM-EDS) and alkali migration (EPMA), however the results obtained for our EPMA results can be used to calculate a lower estimate for the variation in NBO/T between boundary layer and matrix glasses.Using a preeruptive magma temperature of 1000 °C, pressure of 200 MPa and fO2 of NNO+2, as constrained by Andujar et al. 5 and Samaniego et al. 6 , we calculate an Fe 3+ /total Fe ratio of 0.4 using the method of Kress and Carmichael 7 .Using these values, we calculate the average degree of polymerisation as the ratio of non-bridging oxygens to tetrahedrally-coordinated cations (NBO/T 8 ) as 0.18 for the compositional boundary layer glass and 0.24 for the matrix glass.We also estimate the melt viscosity of the boundary layer to be at least 0.5 log Pa s more viscous than the matrix glass melt using the GRD viscosity calculator 9 , however the effect of preferential fracturing through Maxwell criteria10 is inferred to be negligible since we see the same magnitude of surface chemistry variation in samples fragmented well above Tg (estimated using the GRD calculator between 669-676 °C for all measured and calculated glass compositions) and at 25 °C.These modest variations in NBO/T and viscosity may be diminished by reduction in the elastic moduli 11 for the element enrichment and depletion we observe.In summary, we consider this to be a second order effect on the observed localisation of fractures in the boundary layers compared to the orders of magnitude variations in fracture toughness that may be imparted by the nanotexture in the matrix glass 12,13 .

Discussion on the effect of unmixing or nanolitization on remnant melt viscosity and eruption triggering.
In high-resolution SEM-BSE images, matrix textures appear to show that changes in melt .In particular the coarsening of the features surrounding plagioclase microlites (in agreement with prior observations 14 ) and the fining and finally disappearance of the features in compositional boundary layers surrounding pyroxenes crystals (Fig. 3b-c, Supplementary Figures 2-6) provide evidence for the control of local melt chemistry on nanophase size.licateliquid unmixing, crystal-liquid and two-liquid partition coefficients favour partitioning of Fe, Mg, Ca and Ti into the nanophase 15 .The partitioning of Fe and network modifiers, such as Mg and Ca, into a discrete nanophase will cause an increase in the viscosity of the residual interstitial melt 16,17 .A rapid increase in melt viscosity can trigger a transition from viscous flow to brittle failure 18 , and thus the onset of nanolitization or silicate liquid immiscibility may be an effective and sudden eruption trigger 12,19-21.

Supplementary discussion 4 Natural conditions for development of Fe-Ti melt nanotextures
The development of Fe(Ti) nanolites is controlled by the oxidation state and coordination of Fe (determined by the oxygen fugacity 22 ) the concentration of Fe 23 in a silicate melt and the undercooling.The injection of relatively mafic, hot and volatile-rich magma into a more evolved and cooler magma body (known as mafic recharge) is established as a common triggering process for volcanic eruptions 24,25 , including at Tungurahua 6,26 , and a potential source for oxidizing fluids (particularly water) that increase oxygen fugacity and generate high ΔT that may trigger nanolite crystallization 27 and silicate liquid immiscibility in andesitic magmas 28,29 .
Oxygen fugacity conditions increasing from QFM+1 to QFM+2 are shown to favour nanolite crystallization 27 ; a range of QFM+1.5-3 in a high-Fe andesite is modelled to promote silicate liquid immiscibility 30 .These conditions are in the range for arc tectonic settings, although not for plume or mid-ocean ridge volcanism 31 .For nanolites, timescale of nanolite formation are highly dependent on cooling rate, but vary from >1000 minutes (>17 hours) for rhyolite 32 to the first 100s of seconds of cooling for basaltic compositions at high cooling rates 20,33 .For SLI, in the case of binodal CBL-triggered unmixing, timescales may be governed by diffusion rates in the melt 14 with unmixing textures maturing over timescales likely longer than for nanolite formation.
In addition to the basaltic and rhyolitic examples in the main text, the nanotextures documented in this study have been recently recorded in andesitic (Shinmoedake 34 ), trachyandesitic (Tambora 20 ), and trachytic (Fukutoku-oka-no-Ba 27 ) volcanic eruption products.

Comparison of results derived from different measurement techniques.
We use four analytical techniques in the paper: micro-XRF, SEM-EDS and BSE (QEMSCAN), EPMA and XPS to produce three complementary datasets involving different resolution: micro-XRF (bulk chemistry), QEMSCAN+EPMA (microscale surface and bulk chemistry), and XPS (nanoscale surface chemistry).The average results for each sample and each technique (bulk QEMSCAN+EPMA chemistry) are shown in Supplementary Figure 8.
We note some observations from these comparative results.
1. QEMSCAN+EPMA and micro-XRF measurements are in good agreement for all elements 2. Maximum differences between XPS and the other measurements modes for Ca and Al are less than 20%, whereas for Fe and Na differences are up to ~180% and for Mg reach ~800%.
3. The zig-zag patterns of relatively increasing or decreasing concentration between samples shown below are reproduced in both XPS and QEMSCAN+EPMA data for Al, Ca, Fe, and K, but not for Mg and Na.For these latter two elements, the XPS data shows the same relative concentration patterns as for K, but the EPMA+QEMSCAN data are relatively flat.
The similarity of the results in terms of absolute concentration (Al and Ca) and relative concentration between crushed and shock tube samples (Al, Ca and K) in Supplementary Figure 8 shows that a systematic bias is not present between the different measurement techniques.To our knowledge, biases affecting Mg, Fe, and Na (points 2 and 3) in SEM-EDS+EPMA are the well-known glass measurement issues of Na-mobility (and time-dependent depletion in measurements) and Fe oxidation state uncertainty, however we estimated glass chemistry by difference instead of relying on EPMA measurements (see Methods).
EPMA chemistry determination and SEM-based phase mapping are discipline standards in Earth science and the mining industry, respectively, and synthesis of these data types is common in the literature.XPS is a newer technique, but has been increasingly employed in the Earth sciences and other fields in the past decades, and comparisons between nanoscale surfaces measured by XPS and bulk compositions measured by other techniques have been made in numerous studies on volcanic products 35-45.Supplementary discussion 6.

XPS measurement experiments and data analysis integrity
XPS quantification is based on the measurement of peak areas above the spectrum background.
The intensity or area (A) of the peaks, depends on the photon flux (J), the concentration of the atom/ion in the solid (ρ), the cross-section (σ) for photoelectron emission (which depends on the element and energy being considered), instrumental factors (K), and the electron attenuation length (λ), (A= J.ρ.σ.K.λ).In practice, atomic sensitivity factors (F) containing J, σ K λ, are used, and the quantification is given as the fraction of each peak area divided by the sensitivity factor, normalised for all peaks.[i] atomic % = {(Ai/Fi)/Σ(A/F)} where Ai and Fi are the peak area and sensitivity factor of element i. and Σ(A/F) is the sum of the peak area/sensitivity factor ratios for all elements.Therefore, the measurement uncertainty basically springs from how well the peak area is measured, i.e., how well the peak is acquired (high signal to noise ratio is needed) and how well the background is subtracted.For data analysis we used the software casaXPS (SINTEF's licence) and for the quantification we used the whole spectrum area i.e. no peak deconvolution was performed.Therefore, quantification, was not dependent on peak fitting something that contains a significant degree of uncertainty.
To make sure that no errors were introduced due to instrumental factors all samples were measured on the same instrument and the same vacuum level.The spectra were acquired at the same angle of emission (0 o , vertical emission) and the same analyser acceptance angle.In all samples, each peak corresponding to a specific element was acquired at the same acquisition time and the same background type was subtracted for the same peak in all samples.For the quantification we used the instrument provider Wagner sensitivity factors stored in the spectrometer's library.These are empirical and very reliable as are based on measurements performed on standards.The same X-ray power was used for all measurements, all highresolution peaks were acquired at the same pass energy.For the quantification we measured areas of peaks having the same energy to eliminate discrepancies arising from attenuation length differences.As an example, we compared the Mg 2p peaks in all samples and not Mg 1s in one sample with the Mg 2p in another sample.The Mg 1s photoelectron has lower attenuation length and its signal originates from the outermost surface, whilst the Mg 2p has higher attenuation length and originates from slightly deeper in the sample.Thus, we compare the elemental content at the same depth for all samples.Hower, the analysis depth of XPS of scale makes the comparison between surface and bulk evident.
To ensure that no errors attributed to the design of the experiment were introduced in the XPS measurements, the following measures were taken.We analysed 5 different areas of each sample.These gave identical spectra ensuring measurement reproducibility.Each sample was tested for irradiation induced diffusion of alkaline elements (K, Na).For this we performed time resolved experiments by acquiring survey spectra at different acquisition times 1, 5 and 10 minutes.The results showed no such dependence as the spectra exhibited the same peak intensity ratios.To ensure absence of irradiation effects throughout the whole measurement duration for each sample, spectra acquisition started and ended with acquiring survey spectra with the same acquisition parameters.The comparison showed no irradiation effects as the peak intensity ratios of the first and last survey spectrum of the same sample were identical.

Table 1 . X-ray photoelectron spectroscopy (XPS) elemental data from the 2-10 nm surface of experimental ash particles.
Shown for each sample with 1 standard deviation.Acquisition details provided in the Methods.

Table 2 . Bulk and micro-XRF measurements of crushed and fragmented experimental materials.
Due to small sample sizes for fragmented samples, bulk XRF was not possible.However, micro-XRF measurements were conducted for all samples using documented instrument setup and methods 53 .See Methods for details of both techniques.

Table 3 .
QEMSCAN phase quantification results.The mean fraction for both the bulk (Total) and μm-scale surfaces (Surface) for all particles >9 μm diameter are presented.The absolute difference (Abs.Diff.) and relative difference (Rel.Diff.) between the bulk and the surface is given below the phase fractions for each sample, and the standard error of the mean (σx ̅ ) is given below each value.Acquisition details are provided in the Methods.

Table 4 . Mean elemental composition of identified mineral phases.
Data are presented as atomic %, including 1 SD error.Oxygen is calculated via difference.The full EPMA dataset is available in Supplementary data 2 together with the measurement protocol and calibration standards.

Table 5 . Comparison of EPMA measurements for matrix glass and boundary- layer glass and calculated 'ideal' bulk glass.
Due to the defocused (10 μm) beam diameter and pervasive micro-to nanoscale heterogeneities in the matrix, the EPMA measurements (Supplementary Data 3) likely include variable contributions from nanolites or immiscible globules, microlites and compositional gradients from diffusive boundary layers.Therefore, we calculate an 'ideal' bulk glass composition (i.e., equivalent to the glass composition including nanoscale phases that are below QEMSCAN resolution) that we fit to the bulk XRF data (Supplementary Table2) using iterative goalseeking in Microsoft Excel based on measured phase fractions (Supplementary Table4) and crystalline phase compositions (Supplementary Table5).The predicted bulk composition and measured bulk composition are shown in the right-hand columns.at.% is atomic %, avg. is average.

Table 6 . Calculated near-surface and bulk compositions based on combined minerology and EPMA data.
Error is 1 standard error, calculated from the propagation of all errors in EPMA measurements.*indicates that oxygen is a composite derived from multiple elements.Calculations include the hypothetical glass component.